1. Field of the Invention
The present invention relates to an information processing apparatus for deforming a shape and, in particular, to an information processing apparatus for deforming the image of an object to agree with a shape as a target.
2. Description of the Related Art
In the medical field, a medical practitioner (doctor or the like) displays a captured medical image of a patient on a monitor and observes the state and the aging of a lesioned part by interpreting the displayed medical image. Apparatus for generating such a medical image include a roentgenographic apparatus, an X-ray computer tomographic apparatus (X-ray CT), a magnetic resonance imaging apparatus (MRI), a nuclear medicine diagnostic apparatus (e.g., single photon emission computed tomography (SPECT) and positron-emission tomography (PET)), and an ultrasound imaging diagnostic apparatus (US).
When examining a mammary gland, for example, imaging diagnosis can be performed in such a procedure that the position of a lesioned part in a breast is identified on an image captured with an MRI, and then the state of the region is observed using an ultrasound imaging diagnostic apparatus.
In a general imaging protocol at a mammary gland department, in most cases an MRI imaging is performed in the prone position, and an ultrasound imaging is performed in the supine position. At this point, a doctor estimates the position of the lesioned part in the supine position from that of the lesioned part acquired by the MRI image in the prone position in consideration of the deformation of a breast caused by difference in an imaging position.
A very large deformation of the breast caused by difference in an imaging position deviates the position of the lesioned part estimated by the doctor from an actual lesioned part. That causes a problem that the ultrasound image of the lesioned part that is desired to be essentially observed cannot be visualized or a long time is consumed for the search of the lesioned part.
If the MRI imaging is performed in the supine position in which the ultrasound imaging is performed, the problem can be solved, however, the imaging in the supine position is affected by the respiration of a subject to cause a new problem that a clear MRI image required for interpretation cannot be acquired.
If the MRI image virtually captured in the supine position can be generated by subjecting the MRI image captured in the prone position to deformation by image processing, the position of the lesioned part is identified from the deformed MRI image to allow the ultrasound imaging of the lesioned part without consideration of difference in an imaging position.
For example, after the MRI image captured in the prone position is interpreted to acquire the position of the lesioned part on the image, the position of the lesioned part on the virtual MRI image in the supine position can be calculated based on the deformation image from in the prone position to in the supine position. Alternatively, the generated virtual MRI image in the supine position is interpreted to enable directly acquiring the position of the lesioned part on the image.
This can be realized by the following method: T. J. Carter, C. Tanner, W. R. Crum and D. J. Hawkes, “Biomechanical model initialized non-rigid registration for image-guided breast surgery”, 9th Computational Biomechanics for Medicine, 9th MICCAI Conference Workshop. Use of the above method can change the shape of the MRI image in the prone position similar to that of the MRI image in the supine position.
In this method, a virtual MRI image in the supine position is generated from an MRI image in the prone position based on a physical simulation. A deformation registration between the virtual MRI image in the supine position and the MRI image actually captured in the supine position is executed based on the similarity of a pixel value. A process for changing the shape of the MRI image in the prone position into the shape thereof similar to that of the MRI image in the supine position is executed based on the association relationship acquired by the above process.
The following publication discusses a technique for performing an association between the shapes before and after the shape is changed at a high-speed using a statistical motion model (hereinafter referred to as SMM): Y. Hu, D. Morgan, H. U. Ahmed, D. Pendse, M. Sahu, C. Allen, M. Emberton and D. Hawkes, “A statistical motion model based on biomechanical simulations”, Proc. MICCAI 2008, Part I, LNCS 5241, pp. 737-744, 2008.
In the above technique, a deformation shape group is acquired in a case where various parameters related to the deformation of a target object (hereinafter referred to as a deformation parameter) are set by a physical simulation applied to shape data before deformation and the result is subjected to a principal component analysis to generate the SMM. The surface shape data after deformation separately acquired is compared with the shape of a surface part of the SMM to estimate deformation, performing an association between the shapes before and after deformation.
A correct value of the deformation parameter of the target object needs to be acquired in advance to properly act the process based on the method discussed by T. J. Carter and et al. In other words, this causes a problem that the method discussed by T. J. Carter and et al. cannot be applied if the deformation parameter is unknown.
If the deformation parameter is unknown, an approach may be taken in which deformations based on every pattern of the deformation parameter are experimentally performed. This, however, produces a problem that a significant amount of time is required for the experiment of a large number of deformations.
In the method discussed by Y. Hu and et al., deformation is estimated using only the contour shapes of a target object, so that the estimation becomes vague when a smoothly curved shape like a breast surface of the human body is subjected to estimation, and there is a problem that an accurate estimation of deformation cannot be realized.